INFOLOGICAL MODEL OF FACTORS AND FEATURES FOR CHROMOSOMAL PATHOLOGY RECOGNITION POWERED WITH IMAGES OF PATIENTS’ KARYOGRAMS

Abstract

The paper is related to the problem of object recognition in images. Classic approaches to solving similar problems involve extraction of statistical features from images, with further analysis by the means of artificial neural networks. However, such approaches do not achieve plausible results for specific application domains, where classes of objects bear great similarity, while having fundamental logical differences. Another challenge is having abnormal objects, that have to be classified. Given paper proposes an infological model of factors that would allow further development of a multi-criteria decision-making model for solving problems related to object recognition in images. The result of a given paper is an aforementioned proposed infological model of factors that describes objects Chux that represent entities to be recognized; objects Ix that represent an ideal reference value to be used for categorization; classified objects Chx that represent the information about classification results and found deviations from the reference value; a knowledge base BZ that could be used for generating a lexical expression that characterizes the categorization result. Methods of mathematical modeling and mathematical logic have been used during the research for the purposes of designing the infological model. Practical application of the proposed model is the prospect of development a decision support system for chromosomal abnormalities diagnosing powered with images of patient’s karyograms. Such system should be able to accept images of patient’s karyograms (preprocessed samples of patient’s biological material, digitalized by means of a camera embedded into a laboratory microscope) as an input and generate a result of chromosomal pathology detection analysis to the biologist. The novelty of a given paper lies in creation of a basis for object recognition systems powered by logical features of an image, instead of a classic approach that utilizes statistical features and artificial neural networks.